code: def load_Platinum_Adsorption(
      featurizer: Union[
          dc.feat.Featurizer, str
      ] = dc.feat.SineCoulombMatrix(),
      splitter: Union[dc.splits.Splitter, str, None] = "random",
      transformers: List[Union[TransformerGenerator, str]] = [],
      reload: bool = True,
      data_dir: Optional[str] = None,
      save_dir: Optional[str] = None,
      **kwargs,
  ) -> Tuple[
      List[str], Tuple[Dataset, ...], List[dc.trans.Transformer]
  ]:
      """
      Load Platinum Adsorption Dataset
  
      The dataset consist of diffrent configurations of Adsorbates (i.e N and NO)
      on Platinum surface represented as Lattice and their formation energy. There
      are 648 diffrent adsorbate configuration in this datasets represented as Pymatgen
      Structure objects.
  
      1. Pymatgen structure object with site_properties with following key value.
          - "SiteTypes", mentioning if it is a active site "A1" or spectator
              site "S1".
          - "oss", diffrent occupational sites. For spectator sites make it -1.
  
  
      Parameters
      ----------
      featurizer : Featurizer (default LCNNFeaturizer)
          the featurizer to use for processing the data. Reccomended to use
          the LCNNFeaturiser.
      splitter : Splitter (default RandomSplitter)
          the splitter to use for splitting the data into training, validation, and
          test sets.  Alternatively you can pass one of the names from
          dc.molnet.splitters as a shortcut.  If this is None, all the data will
          be included in a single dataset.
      transformers : list of TransformerGenerators or strings. the Transformers to
          apply to the data and appropritate featuriser. Does'nt require any
          transformation for LCNN_featuriser
      reload : bool
          if True, the first call for a particular featurizer and splitter will cache
          the datasets to disk, and subsequent calls will reload the cached datasets.
      data_dir : str
          a directory to save the raw data in
      save_dir : str, optional (default None)
          a directory to save the dataset in
  
      References
      ----------
      .. [1] Jonathan Lym, Geun Ho G. "Lattice Convolutional Neural Network Modeling of Adsorbate
          Coverage Effects"J. Phys. Chem. C 2019, 123, 18951−18959
  
      Examples
      --------
      >>>
      >> import deepchem as dc
      >> tasks, datasets, transformers = load_Platinum_Adsorption(
      >>    reload=True,
      >>    data_dir=data_path,
      >>    save_dir=data_path,
      >>    featurizer_kwargs=feat_args)
      >> train_dataset, val_dataset, test_dataset = datasets
      """
  
      loader = _PtAdsorptionLoader(
          featurizer,
          splitter,
          transformers,
          PLATINUM_TASKS,
          data_dir,
          save_dir,
          **kwargs,
      )
      return loader.load_dataset("LCNN_feat", reload)
  
file_path: deepchem/deepchem/molnet/load_function/material_datasets/load_Pt_NO_surface_adsorbate_energy.py
imports: import numpy
  import os
  import deepchem
  from deepchem.molnet.load_function.molnet_loader import TransformerGenerator, _MolnetLoader
  from deepchem.data import Dataset
  from typing import List, Optional, Tuple, Union
label_desc: Use image node_id 1 for calling a global function with example usage: load_Platinum_Adsorption(featurizer, splitter, transformers, reload, data_dir, save_dir) and returns: loader